Schlumberger Technology Corporation

Westerngeco L.l.p.

Hugues A Djikpesse

Michael David Prange

Benoit Couet

William John Bailey

Mohamed-Rabigh Khodja

Kashif Rashid

Bikash Kumar Sinha

Henri-Pierre Valero

David J Wilkinson

Vladimir L'vovich Druskin

Henry Menkiti

Darrell Coles

Sebastien Duchenne

Saumil Ambani

Eren Cetinkaya

Michael A Elliott

Managing oilfield operations include obtaining a subsurface model including a fracture design model having an fracture property with an uncertain value. A set of representative values that represent uncertainty in the fracture property is obtained and used to solve an oilfield optimization problem with a control variable to obtain a solution. The solution includes an optimal value for the control variable. Based on the solution, an oilfield design is generated and stored.

Multistage oilfield design optimization under uncertainty

A Bayesian methodology is described for designing experiments or surveys that are improved by utilizing available prior information to guide the design toward maximally reducing posterior uncertainties in the interpretation of the future experiment. Synthetic geophysical tomography examples are used to illustrate benefits of this approach.

Guided bayesian experimental design

Apparatus and methods of categorizing a subterranean formation including collecting data related to the formation, performing an algorithm comprising guided Bayesian survey design, identifying data observations to collect, and collecting additional data observations related to the formation. In some embodiments, the performing comprises forming a matrix or loading a matrix or both. Apparatus and methods of categorizing a subterranean formation including collecting data related to the formation, performing an algorithm comprising guided Bayesian survey design, identifying data observations to use, and processing the data observations wherein the observations require less processing time than if no algorithm were performed.

Large survey compressive designs

Complex-valued sensitivity data structures corresponding to respective candidate survey settings are provided, where the sensitivity data structures relate measurement data associated with a target structure to at least one parameter of a model of the target structure. Based on the sensitivity data structures, a subset of the candidate survey settings is selected according to a criterion for enhancing resolution in characterizing the target structure.

Selecting a survey setting for characterizing a target structure

A method for optimizing expensive functions with expensive nonlinear constraints. The method includes selecting sample data for evaluating an expensive function of a simulation, generating a function proxy model for the expensive function and a constraint proxy model for an expensive nonlinear constraint of the expensive function using an approximation scheme, calculating a first solution point for the simulation using the proxy models, and evaluating the expensive function at the first solution point using the sample data. When the expensive function and the proxy models do not converge at the first solution point, the method further includes adding the first solution point to the sample data for updating the proxy models. The method further includes repeating the calculation and evaluation of solution points until the expensive function and the proxy models converge and, following convergence, identifying an optimal solution of the function proxy model and the constraint proxy model.

Proxy methods for expensive function optimization with expensive nonlinear constraints

Guided Bayesian experimental design

The invention relates to a method of performing an oilfield operation of an oilfield having at least one well having a wellbore penetrating a subterranean formation for extracting fluid from an underground reservoir therein. The method steps include analyzing the oilfield operation to generate a decision tree comprising a first decision and a second decision, wherein a first outcome of the first decision dictates acquiring information relevant to the second decision, formulating a figure of merit of the oilfield operation according to the decision tree based on the information and uncertainties associated with the oilfield, determining a value of the figure of merit by modeling the oilfield operation using statistical sampling, and performing the oilfield operation upon making the first decision based on the value of the figure of merit.

Valuing future information under uncertainty

An integrated framework is described for automating some or all of mud slowness estimation for both fast and slow formations. An estimation of fluid slowness based on monopole radial profiling is calculated if conditions permit. Alternatively, an estimation of fluid slowness based on Scholte wave slowness is estimated if conditions do not permit calculation based on monopole radial profiling. Tool standoff may also be estimated based on monopole radial profiling.

Automated mud slowness estimation

A method is disclosed for performing optimal gridding in reservoir simulation, the method comprising: establishing an optimal coarse grid proxy that can replace all or parts of a fine grid with a coarse grid while preserving an accuracy of a predefined simulation model output, the step of establishing an optimal coarse grid proxy including finding, by using an optimizer, a best fit of a coarse grid output to the output of a training set.

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Cambridge, MA, United States of America

Hugues A Djikpesse